Evaluation of Holographic Imaging Cytometer HoloMonitor M4® Motility Applications

Cytometry Part A, 2018 | Y. Zhang and R. L. Judson

The HoloMonitor software modules for cell tracking and wound healing analysis were evaluated and compared to the more conventional methods transwell migration and transwell invasion. Both HoloMonitor modules were found to be well-correlated with established standards, yielded reproducible results, and at the same time offered distinct advantages. The wound healing assay was the most tractable and automated method with good reproducibility, while the cell tracking module enabled identification of hypermobile subpopulations.

Read more

Bi-allelic Loss of CDKN2A Initiates Melanoma Invasion via BRN2 Activation

Cancer Cell, 2018 | Hanlin Zeng et al.

Utilizing precision genetic engineering and PHI’s HoloMonitor technology, scientists at University of California, San Francisco, have for the first time been able to monitor and map how mutations break down the genetic protection against skin cancer, allowing harmless moles to transform into invasive skin cancer.

Read more

Quantification of mammalian tumor cell state plasticity with digital holographic cytometry

SPIE Conference Proceedings, 2018 | M Hejna, A Jorapur, Y Zhang, J S Song and R L Judson

Working with a HoloMonitor M4 digital holographic cytometry platform, we have established a machine learning-based pipeline for high accuracy and label-free classification of adherent cells.

Read more

Combined activation of MAP kinase pathway and β-catenin signaling cause deep penetrating nevi

Nature Communications, 2017 | I. Yeh et al.

HoloMonitor was used to measure cell volume. Together with other methods the results identify DPN (deep penetrating nevus) as an intermediate melanocytic neoplasm, with a progression stage positioned between benign nevus and DPN-like melanoma.

Read more

High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells

Scientific Reports, 2017 | M. Hejna et al.

The authors used machine learning to develop a method for robust and kinetic label-free classification of single adherent cells info functional states.

Read more